Empirical Exercise 5: Beauty
London School of Economics and Political Science
January 26, 2026
If beauty doesn’t affect productivity, why would employers pay more?
Could you profit by hiring less attractive workers at lower wages?
Effect of below-average looks vs. one year of education on log wages. Error bars show 95% confidence intervals.
Being rated below-average costs as much as missing 2.5 years of education
Gray = Productive (efficient) | Gold = Potentially discriminatory
If \(\alpha_1 > 0\) after controls \(\Rightarrow\) overconfidence
We need an experiment to separate these channels
Solving computer mazes (15-minute work period)
| Treatment | Resume | Photo | Phone | Face-to-Face |
|---|---|---|---|---|
| B (Baseline) | ✓ | — | — | — |
| V (Visual) | ✓ | ✓ | — | — |
| O (Oral) | ✓ | — | ✓ | — |
| VO (Both) | ✓ | ✓ | ✓ | — |
| FTF (Face-to-Face) | ✓ | ✓ | ✓ | ✓ |
Each employer rates 5 different workers → Employer fixed effects
Randomization of treatment ≠ randomization of beauty
\[ \begin{aligned} \text{Pay}_j &= 100 \times \text{Actual}_j \\ &- 40|\text{Estimate}_j - \text{Actual}_j| \\ &+ \sum_{i}\text{Wage}_{ij} \end{aligned} \]
\[\text{Pay}_i = 4000 - \sum_{j=1}^{5} 40|\text{Wage}_{ij} - \text{Actual}_j| \]
\[ \widehat{\log(\text{actual})}_j = -\underset{(0.039)}{0.018} \cdot \text{beauty}_j - \underset{(0.012)}{0.039} \cdot \text{age}_j + \underset{(0.087)}{0.372} \cdot \mathbb{1}[j \text{ is male}] + X_{j}' \hat{\Gamma} \]
Beauty does not affect productivity
Residualized confidence (after controlling for actual and projected ability) vs. beauty. The positive slope indicates overconfidence.
+17% confidence per SD beauty, even after controlling for actual ability
Beauty STILL predicts higher confidence. This is OVERCONFIDENCE
Effect of log confidence on log wage by treatment. Error bars show 95% CI. Confidence only matters in treatments with audio interaction.
Confidence is communicated through speech, not appearance
Effect of one SD increase in beauty on log wage by treatment. Error bars show 95% CI. No premium without interaction; premium emerges with visual OR oral contact.
No premium without interaction
Decomposition coefficients from pooled regression. Visual and oral channels both contribute, but with negative interaction (channels overlap).
Face-to-face ≠ Visual + Oral (diminishing returns)
\[ \underbrace{6.8\%}_{\text{Visual}} + \underbrace{11.3\%}_{\text{Oral}} + \underbrace{2.7\%}_{\text{Confidence}} - \underbrace{7.1\%}_{\text{Overlap}} \approx 13.7\% \]
The negative overlap term means channels have diminishing returns—face-to-face ≠ visual + oral
The premium reflects beliefs about productivity, not tastes for beauty
Next Week: Exploiting time variation—Panel data methods